Degree of Parkinson's Disease Severity Estimation Based on Speech Signal Processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU119577" target="_blank" >RIV/00216305:26220/16:PU119577 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7760930" target="_blank" >https://ieeexplore.ieee.org/document/7760930</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2016.7760930" target="_blank" >10.1109/TSP.2016.7760930</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Degree of Parkinson's Disease Severity Estimation Based on Speech Signal Processing

Popis výsledku v původním jazyce

This paper deals with Parkinson’s disease (PD) severity estimation according to the Unified Parkinson’s Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS III ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS III scores and scores predicted by the proposed methodology it was shown information extracted through variety of speech tasks can be used to estimate PD severity. of PD severity.

Název v anglickém jazyce

Degree of Parkinson's Disease Severity Estimation Based on Speech Signal Processing

Popis výsledku anglicky

This paper deals with Parkinson’s disease (PD) severity estimation according to the Unified Parkinson’s Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS III ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS III scores and scores predicted by the proposed methodology it was shown information extracted through variety of speech tasks can be used to estimate PD severity. of PD severity.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 39th International Conference on Telecommunication and Signal Processing, TSP 2016

ISBN

978-1-5090-1287-9

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

503-506

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Vídeň

Datum konání akce

27. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000390164000109